In [1]:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

0. Problem biznesowy¶

Celem projektu jest klasteryzacja danych zebranych z czujników znajdjących się w smartfonach (akcelerometr, zyroskop, ...).
Projekt przewidywał stworzenie modelu przewidującego aktywności: chodzenie, wchodzenie po schodach, schodzenie schodami, siedzenie, stanie, leżenie.
Dataset został zebrany z 30 osób (później subjects) wykonujących wymienione aktywności.

1. wczytanie i podział danych¶

In [2]:
data = pd.read_csv('../data/data.csv')
In [3]:
from sklearn.model_selection import train_test_split
our_data, validator_data = train_test_split(data, test_size=0.2, random_state=42)

2. wstepna analiza¶

In [4]:
data.head()
Out[4]:
tBodyAcc-mean()-X tBodyAcc-mean()-Y tBodyAcc-mean()-Z tBodyAcc-std()-X tBodyAcc-std()-Y tBodyAcc-std()-Z tBodyAcc-mad()-X tBodyAcc-mad()-Y tBodyAcc-mad()-Z tBodyAcc-max()-X ... fBodyBodyGyroJerkMag-skewness() fBodyBodyGyroJerkMag-kurtosis() angle(tBodyAccMean,gravity) angle(tBodyAccJerkMean),gravityMean) angle(tBodyGyroMean,gravityMean) angle(tBodyGyroJerkMean,gravityMean) angle(X,gravityMean) angle(Y,gravityMean) angle(Z,gravityMean) subject
0 0.288585 -0.020294 -0.132905 -0.995279 -0.983111 -0.913526 -0.995112 -0.983185 -0.923527 -0.934724 ... -0.298676 -0.710304 -0.112754 0.030400 -0.464761 -0.018446 -0.841247 0.179941 -0.058627 1
1 0.278419 -0.016411 -0.123520 -0.998245 -0.975300 -0.960322 -0.998807 -0.974914 -0.957686 -0.943068 ... -0.595051 -0.861499 0.053477 -0.007435 -0.732626 0.703511 -0.844788 0.180289 -0.054317 1
2 0.279653 -0.019467 -0.113462 -0.995380 -0.967187 -0.978944 -0.996520 -0.963668 -0.977469 -0.938692 ... -0.390748 -0.760104 -0.118559 0.177899 0.100699 0.808529 -0.848933 0.180637 -0.049118 1
3 0.279174 -0.026201 -0.123283 -0.996091 -0.983403 -0.990675 -0.997099 -0.982750 -0.989302 -0.938692 ... -0.117290 -0.482845 -0.036788 -0.012892 0.640011 -0.485366 -0.848649 0.181935 -0.047663 1
4 0.276629 -0.016570 -0.115362 -0.998139 -0.980817 -0.990482 -0.998321 -0.979672 -0.990441 -0.942469 ... -0.351471 -0.699205 0.123320 0.122542 0.693578 -0.615971 -0.847865 0.185151 -0.043892 1

5 rows × 562 columns

In [7]:
firstrow = data.iloc[0]
for i in range(len(firstrow)):
    print(firstrow.index[i], firstrow.iloc[i])
tBodyAcc-mean()-X 0.28858451
tBodyAcc-mean()-Y -0.020294171
tBodyAcc-mean()-Z -0.13290514
tBodyAcc-std()-X -0.9952786
tBodyAcc-std()-Y -0.98311061
tBodyAcc-std()-Z -0.91352645
tBodyAcc-mad()-X -0.99511208
tBodyAcc-mad()-Y -0.98318457
tBodyAcc-mad()-Z -0.92352702
tBodyAcc-max()-X -0.93472378
tBodyAcc-max()-Y -0.56737807
tBodyAcc-max()-Z -0.74441253
tBodyAcc-min()-X 0.85294738
tBodyAcc-min()-Y 0.68584458
tBodyAcc-min()-Z 0.81426278
tBodyAcc-sma() -0.96552279
tBodyAcc-energy()-X -0.99994465
tBodyAcc-energy()-Y -0.99986303
tBodyAcc-energy()-Z -0.99461218
tBodyAcc-iqr()-X -0.99423081
tBodyAcc-iqr()-Y -0.98761392
tBodyAcc-iqr()-Z -0.94321999
tBodyAcc-entropy()-X -0.40774707
tBodyAcc-entropy()-Y -0.67933751
tBodyAcc-entropy()-Z -0.60212187
tBodyAcc-arCoeff()-X,1 0.92929351
tBodyAcc-arCoeff()-X,2 -0.85301114
tBodyAcc-arCoeff()-X,3 0.35990976
tBodyAcc-arCoeff()-X,4 -0.058526382
tBodyAcc-arCoeff()-Y,1 0.25689154
tBodyAcc-arCoeff()-Y,2 -0.22484763
tBodyAcc-arCoeff()-Y,3 0.26410572
tBodyAcc-arCoeff()-Y,4 -0.09524563
tBodyAcc-arCoeff()-Z,1 0.27885143
tBodyAcc-arCoeff()-Z,2 -0.46508457
tBodyAcc-arCoeff()-Z,3 0.49193596
tBodyAcc-arCoeff()-Z,4 -0.19088356
tBodyAcc-correlation()-X,Y 0.37631389
tBodyAcc-correlation()-X,Z 0.43512919
tBodyAcc-correlation()-Y,Z 0.66079033
tGravityAcc-mean()-X 0.96339614
tGravityAcc-mean()-Y -0.14083968
tGravityAcc-mean()-Z 0.11537494
tGravityAcc-std()-X -0.98524969
tGravityAcc-std()-Y -0.98170843
tGravityAcc-std()-Z -0.87762497
tGravityAcc-mad()-X -0.98500137
tGravityAcc-mad()-Y -0.98441622
tGravityAcc-mad()-Z -0.89467735
tGravityAcc-max()-X 0.89205451
tGravityAcc-max()-Y -0.16126549
tGravityAcc-max()-Z 0.12465977
tGravityAcc-min()-X 0.97743631
tGravityAcc-min()-Y -0.12321341
tGravityAcc-min()-Z 0.056482734
tGravityAcc-sma() -0.37542596
tGravityAcc-energy()-X 0.89946864
tGravityAcc-energy()-Y -0.97090521
tGravityAcc-energy()-Z -0.97551037
tGravityAcc-iqr()-X -0.98432539
tGravityAcc-iqr()-Y -0.98884915
tGravityAcc-iqr()-Z -0.91774264
tGravityAcc-entropy()-X -1.0
tGravityAcc-entropy()-Y -1.0
tGravityAcc-entropy()-Z 0.11380614
tGravityAcc-arCoeff()-X,1 -0.590425
tGravityAcc-arCoeff()-X,2 0.5911463
tGravityAcc-arCoeff()-X,3 -0.59177346
tGravityAcc-arCoeff()-X,4 0.59246928
tGravityAcc-arCoeff()-Y,1 -0.74544878
tGravityAcc-arCoeff()-Y,2 0.72086167
tGravityAcc-arCoeff()-Y,3 -0.71237239
tGravityAcc-arCoeff()-Y,4 0.71130003
tGravityAcc-arCoeff()-Z,1 -0.99511159
tGravityAcc-arCoeff()-Z,2 0.99567491
tGravityAcc-arCoeff()-Z,3 -0.99566759
tGravityAcc-arCoeff()-Z,4 0.99165268
tGravityAcc-correlation()-X,Y 0.57022164
tGravityAcc-correlation()-X,Z 0.43902735
tGravityAcc-correlation()-Y,Z 0.98691312
tBodyAccJerk-mean()-X 0.077996345
tBodyAccJerk-mean()-Y 0.0050008031
tBodyAccJerk-mean()-Z -0.067830808
tBodyAccJerk-std()-X -0.99351906
tBodyAccJerk-std()-Y -0.98835999
tBodyAccJerk-std()-Z -0.99357497
tBodyAccJerk-mad()-X -0.99448763
tBodyAccJerk-mad()-Y -0.98620664
tBodyAccJerk-mad()-Z -0.99281835
tBodyAccJerk-max()-X -0.9851801
tBodyAccJerk-max()-Y -0.99199423
tBodyAccJerk-max()-Z -0.99311887
tBodyAccJerk-min()-X 0.98983471
tBodyAccJerk-min()-Y 0.99195686
tBodyAccJerk-min()-Z 0.9905192
tBodyAccJerk-sma() -0.99352201
tBodyAccJerk-energy()-X -0.99993487
tBodyAccJerk-energy()-Y -0.99982045
tBodyAccJerk-energy()-Z -0.99987846
tBodyAccJerk-iqr()-X -0.99436404
tBodyAccJerk-iqr()-Y -0.98602487
tBodyAccJerk-iqr()-Z -0.98923361
tBodyAccJerk-entropy()-X -0.81994925
tBodyAccJerk-entropy()-Y -0.79304645
tBodyAccJerk-entropy()-Z -0.88885295
tBodyAccJerk-arCoeff()-X,1 1.0
tBodyAccJerk-arCoeff()-X,2 -0.22074703
tBodyAccJerk-arCoeff()-X,3 0.63683075
tBodyAccJerk-arCoeff()-X,4 0.38764356
tBodyAccJerk-arCoeff()-Y,1 0.24140146
tBodyAccJerk-arCoeff()-Y,2 -0.052252848
tBodyAccJerk-arCoeff()-Y,3 0.2641772
tBodyAccJerk-arCoeff()-Y,4 0.37343945
tBodyAccJerk-arCoeff()-Z,1 0.34177752
tBodyAccJerk-arCoeff()-Z,2 -0.56979119
tBodyAccJerk-arCoeff()-Z,3 0.26539882
tBodyAccJerk-arCoeff()-Z,4 -0.47787489
tBodyAccJerk-correlation()-X,Y -0.3853005
tBodyAccJerk-correlation()-X,Z 0.033643943
tBodyAccJerk-correlation()-Y,Z -0.12651082
tBodyGyro-mean()-X -0.0061008489
tBodyGyro-mean()-Y -0.031364791
tBodyGyro-mean()-Z 0.1077254
tBodyGyro-std()-X -0.98531027
tBodyGyro-std()-Y -0.97662344
tBodyGyro-std()-Z -0.99220528
tBodyGyro-mad()-X -0.98458626
tBodyGyro-mad()-Y -0.97635262
tBodyGyro-mad()-Z -0.99236164
tBodyGyro-max()-X -0.86704374
tBodyGyro-max()-Y -0.93378602
tBodyGyro-max()-Z -0.74756618
tBodyGyro-min()-X 0.84730796
tBodyGyro-min()-Y 0.91489534
tBodyGyro-min()-Z 0.83084054
tBodyGyro-sma() -0.96718428
tBodyGyro-energy()-X -0.99957831
tBodyGyro-energy()-Y -0.99935432
tBodyGyro-energy()-Z -0.99976339
tBodyGyro-iqr()-X -0.98343808
tBodyGyro-iqr()-Y -0.97861401
tBodyGyro-iqr()-Z -0.99296558
tBodyGyro-entropy()-X 0.082631682
tBodyGyro-entropy()-Y 0.20226765
tBodyGyro-entropy()-Z -0.16875669
tBodyGyro-arCoeff()-X,1 0.096323236
tBodyGyro-arCoeff()-X,2 -0.27498511
tBodyGyro-arCoeff()-X,3 0.49864419
tBodyGyro-arCoeff()-X,4 -0.22031685
tBodyGyro-arCoeff()-Y,1 1.0
tBodyGyro-arCoeff()-Y,2 -0.97297139
tBodyGyro-arCoeff()-Y,3 0.31665451
tBodyGyro-arCoeff()-Y,4 0.37572641
tBodyGyro-arCoeff()-Z,1 0.72339919
tBodyGyro-arCoeff()-Z,2 -0.77111201
tBodyGyro-arCoeff()-Z,3 0.69021323
tBodyGyro-arCoeff()-Z,4 -0.33183104
tBodyGyro-correlation()-X,Y 0.70958377
tBodyGyro-correlation()-X,Z 0.13487336
tBodyGyro-correlation()-Y,Z 0.30109948
tBodyGyroJerk-mean()-X -0.0991674
tBodyGyroJerk-mean()-Y -0.055517369
tBodyGyroJerk-mean()-Z -0.061985797
tBodyGyroJerk-std()-X -0.99211067
tBodyGyroJerk-std()-Y -0.99251927
tBodyGyroJerk-std()-Z -0.99205528
tBodyGyroJerk-mad()-X -0.99216475
tBodyGyroJerk-mad()-Y -0.99494156
tBodyGyroJerk-mad()-Z -0.99261905
tBodyGyroJerk-max()-X -0.99015585
tBodyGyroJerk-max()-Y -0.98674277
tBodyGyroJerk-max()-Z -0.99204155
tBodyGyroJerk-min()-X 0.99442876
tBodyGyroJerk-min()-Y 0.99175581
tBodyGyroJerk-min()-Z 0.98935195
tBodyGyroJerk-sma() -0.99445335
tBodyGyroJerk-energy()-X -0.99993755
tBodyGyroJerk-energy()-Y -0.9999535
tBodyGyroJerk-energy()-Z -0.99992294
tBodyGyroJerk-iqr()-X -0.99229974
tBodyGyroJerk-iqr()-Y -0.99693892
tBodyGyroJerk-iqr()-Z -0.99224298
tBodyGyroJerk-entropy()-X -0.58985096
tBodyGyroJerk-entropy()-Y -0.68845905
tBodyGyroJerk-entropy()-Z -0.57210686
tBodyGyroJerk-arCoeff()-X,1 0.29237634
tBodyGyroJerk-arCoeff()-X,2 -0.36199802
tBodyGyroJerk-arCoeff()-X,3 0.40554269
tBodyGyroJerk-arCoeff()-X,4 -0.039006951
tBodyGyroJerk-arCoeff()-Y,1 0.98928381
tBodyGyroJerk-arCoeff()-Y,2 -0.41456048
tBodyGyroJerk-arCoeff()-Y,3 0.39160251
tBodyGyroJerk-arCoeff()-Y,4 0.28225087
tBodyGyroJerk-arCoeff()-Z,1 0.92726984
tBodyGyroJerk-arCoeff()-Z,2 -0.57237001
tBodyGyroJerk-arCoeff()-Z,3 0.6916192
tBodyGyroJerk-arCoeff()-Z,4 0.46828982
tBodyGyroJerk-correlation()-X,Y -0.13107697
tBodyGyroJerk-correlation()-X,Z -0.087159695
tBodyGyroJerk-correlation()-Y,Z 0.33624748
tBodyAccMag-mean() -0.95943388
tBodyAccMag-std() -0.9505515
tBodyAccMag-mad() -0.95799295
tBodyAccMag-max() -0.94630524
tBodyAccMag-min() -0.99255572
tBodyAccMag-sma() -0.95943388
tBodyAccMag-energy() -0.99849285
tBodyAccMag-iqr() -0.9576374
tBodyAccMag-entropy() -0.23258164
tBodyAccMag-arCoeff()1 -0.17317874
tBodyAccMag-arCoeff()2 -0.02289666
tBodyAccMag-arCoeff()3 0.094831568
tBodyAccMag-arCoeff()4 0.19181715
tGravityAccMag-mean() -0.95943388
tGravityAccMag-std() -0.9505515
tGravityAccMag-mad() -0.95799295
tGravityAccMag-max() -0.94630524
tGravityAccMag-min() -0.99255572
tGravityAccMag-sma() -0.95943388
tGravityAccMag-energy() -0.99849285
tGravityAccMag-iqr() -0.9576374
tGravityAccMag-entropy() -0.23258164
tGravityAccMag-arCoeff()1 -0.17317874
tGravityAccMag-arCoeff()2 -0.02289666
tGravityAccMag-arCoeff()3 0.094831568
tGravityAccMag-arCoeff()4 0.19181715
tBodyAccJerkMag-mean() -0.99330586
tBodyAccJerkMag-std() -0.99433641
tBodyAccJerkMag-mad() -0.99450037
tBodyAccJerkMag-max() -0.99278399
tBodyAccJerkMag-min() -0.99120847
tBodyAccJerkMag-sma() -0.99330586
tBodyAccJerkMag-energy() -0.99989188
tBodyAccJerkMag-iqr() -0.9929337
tBodyAccJerkMag-entropy() -0.86341476
tBodyAccJerkMag-arCoeff()1 0.28308522
tBodyAccJerkMag-arCoeff()2 -0.23730869
tBodyAccJerkMag-arCoeff()3 -0.10543219
tBodyAccJerkMag-arCoeff()4 -0.038212313
tBodyGyroMag-mean() -0.96895908
tBodyGyroMag-std() -0.96433518
tBodyGyroMag-mad() -0.95724477
tBodyGyroMag-max() -0.97505986
tBodyGyroMag-min() -0.99155366
tBodyGyroMag-sma() -0.96895908
tBodyGyroMag-energy() -0.99928646
tBodyGyroMag-iqr() -0.94976582
tBodyGyroMag-entropy() 0.072579035
tBodyGyroMag-arCoeff()1 0.57251142
tBodyGyroMag-arCoeff()2 -0.73860219
tBodyGyroMag-arCoeff()3 0.21257776
tBodyGyroMag-arCoeff()4 0.43340495
tBodyGyroJerkMag-mean() -0.99424782
tBodyGyroJerkMag-std() -0.99136761
tBodyGyroJerkMag-mad() -0.99314298
tBodyGyroJerkMag-max() -0.98893563
tBodyGyroJerkMag-min() -0.99348603
tBodyGyroJerkMag-sma() -0.99424782
tBodyGyroJerkMag-energy() -0.99994898
tBodyGyroJerkMag-iqr() -0.99454718
tBodyGyroJerkMag-entropy() -0.61976763
tBodyGyroJerkMag-arCoeff()1 0.29284049
tBodyGyroJerkMag-arCoeff()2 -0.1768892
tBodyGyroJerkMag-arCoeff()3 -0.14577921
tBodyGyroJerkMag-arCoeff()4 -0.12407233
fBodyAcc-mean()-X -0.99478319
fBodyAcc-mean()-Y -0.9829841
fBodyAcc-mean()-Z -0.93926865
fBodyAcc-std()-X -0.99542175
fBodyAcc-std()-Y -0.98313297
fBodyAcc-std()-Z -0.90616498
fBodyAcc-mad()-X -0.99688864
fBodyAcc-mad()-Y -0.98451927
fBodyAcc-mad()-Z -0.932082
fBodyAcc-max()-X -0.99375634
fBodyAcc-max()-Y -0.98316285
fBodyAcc-max()-Z -0.88505422
fBodyAcc-min()-X -0.99396185
fBodyAcc-min()-Y -0.99344611
fBodyAcc-min()-Z -0.92342772
fBodyAcc-sma() -0.97473271
fBodyAcc-energy()-X -0.99996838
fBodyAcc-energy()-Y -0.99968911
fBodyAcc-energy()-Z -0.99489148
fBodyAcc-iqr()-X -0.99592602
fBodyAcc-iqr()-Y -0.98970889
fBodyAcc-iqr()-Z -0.98799115
fBodyAcc-entropy()-X -0.94635692
fBodyAcc-entropy()-Y -0.90474776
fBodyAcc-entropy()-Z -0.59130248
fBodyAcc-maxInds-X -1.0
fBodyAcc-maxInds-Y -1.0
fBodyAcc-maxInds-Z -1.0
fBodyAcc-meanFreq()-X 0.2524829
fBodyAcc-meanFreq()-Y 0.13183575
fBodyAcc-meanFreq()-Z -0.052050251
fBodyAcc-skewness()-X 0.14205056
fBodyAcc-kurtosis()-X -0.1506825
fBodyAcc-skewness()-Y -0.22054694
fBodyAcc-kurtosis()-Y -0.55873853
fBodyAcc-skewness()-Z 0.24676868
fBodyAcc-kurtosis()-Z -0.0074155206
fBodyAcc-bandsEnergy()-1,8 -0.99996279
fBodyAcc-bandsEnergy()-9,16 -0.9999865
fBodyAcc-bandsEnergy()-17,24 -0.99997907
fBodyAcc-bandsEnergy()-25,32 -0.99996244
fBodyAcc-bandsEnergy()-33,40 -0.99993222
fBodyAcc-bandsEnergy()-41,48 -0.99972512
fBodyAcc-bandsEnergy()-49,56 -0.99967039
fBodyAcc-bandsEnergy()-57,64 -0.99998582
fBodyAcc-bandsEnergy()-1,16 -0.99996867
fBodyAcc-bandsEnergy()-17,32 -0.99997686
fBodyAcc-bandsEnergy()-33,48 -0.99986966
fBodyAcc-bandsEnergy()-49,64 -0.99977613
fBodyAcc-bandsEnergy()-1,24 -0.99997115
fBodyAcc-bandsEnergy()-25,48 -0.99991925
fBodyAcc-bandsEnergy()-1,8.1 -0.9996568
fBodyAcc-bandsEnergy()-9,16.1 -0.99986046
fBodyAcc-bandsEnergy()-17,24.1 -0.99986695
fBodyAcc-bandsEnergy()-25,32.1 -0.99986301
fBodyAcc-bandsEnergy()-33,40.1 -0.99973783
fBodyAcc-bandsEnergy()-41,48.1 -0.9997322
fBodyAcc-bandsEnergy()-49,56.1 -0.99949261
fBodyAcc-bandsEnergy()-57,64.1 -0.99981364
fBodyAcc-bandsEnergy()-1,16.1 -0.99968182
fBodyAcc-bandsEnergy()-17,32.1 -0.9998394
fBodyAcc-bandsEnergy()-33,48.1 -0.99973823
fBodyAcc-bandsEnergy()-49,64.1 -0.99961197
fBodyAcc-bandsEnergy()-1,24.1 -0.99968721
fBodyAcc-bandsEnergy()-25,48.1 -0.99983863
fBodyAcc-bandsEnergy()-1,8.2 -0.99359234
fBodyAcc-bandsEnergy()-9,16.2 -0.99947584
fBodyAcc-bandsEnergy()-17,24.2 -0.99966204
fBodyAcc-bandsEnergy()-25,32.2 -0.9996423
fBodyAcc-bandsEnergy()-33,40.2 -0.99929341
fBodyAcc-bandsEnergy()-41,48.2 -0.99789222
fBodyAcc-bandsEnergy()-49,56.2 -0.99593249
fBodyAcc-bandsEnergy()-57,64.2 -0.99514642
fBodyAcc-bandsEnergy()-1,16.2 -0.9947399
fBodyAcc-bandsEnergy()-17,32.2 -0.99968826
fBodyAcc-bandsEnergy()-33,48.2 -0.99892456
fBodyAcc-bandsEnergy()-49,64.2 -0.99567134
fBodyAcc-bandsEnergy()-1,24.2 -0.99487731
fBodyAcc-bandsEnergy()-25,48.2 -0.99945439
fBodyAccJerk-mean()-X -0.99233245
fBodyAccJerk-mean()-Y -0.98716991
fBodyAccJerk-mean()-Z -0.98969609
fBodyAccJerk-std()-X -0.99582068
fBodyAccJerk-std()-Y -0.99093631
fBodyAccJerk-std()-Z -0.99705167
fBodyAccJerk-mad()-X -0.99380547
fBodyAccJerk-mad()-Y -0.99051869
fBodyAccJerk-mad()-Z -0.99699279
fBodyAccJerk-max()-X -0.99673689
fBodyAccJerk-max()-Y -0.99197516
fBodyAccJerk-max()-Z -0.99324167
fBodyAccJerk-min()-X -0.99834907
fBodyAccJerk-min()-Y -0.99110842
fBodyAccJerk-min()-Z -0.95988537
fBodyAccJerk-sma() -0.99051499
fBodyAccJerk-energy()-X -0.99993475
fBodyAccJerk-energy()-Y -0.99982048
fBodyAccJerk-energy()-Z -0.99988449
fBodyAccJerk-iqr()-X -0.99302626
fBodyAccJerk-iqr()-Y -0.99137339
fBodyAccJerk-iqr()-Z -0.99623962
fBodyAccJerk-entropy()-X -1.0
fBodyAccJerk-entropy()-Y -1.0
fBodyAccJerk-entropy()-Z -1.0
fBodyAccJerk-maxInds-X 1.0
fBodyAccJerk-maxInds-Y -0.24
fBodyAccJerk-maxInds-Z -1.0
fBodyAccJerk-meanFreq()-X 0.87038451
fBodyAccJerk-meanFreq()-Y 0.210697
fBodyAccJerk-meanFreq()-Z 0.26370789
fBodyAccJerk-skewness()-X -0.70368577
fBodyAccJerk-kurtosis()-X -0.90374251
fBodyAccJerk-skewness()-Y -0.58257362
fBodyAccJerk-kurtosis()-Y -0.93631005
fBodyAccJerk-skewness()-Z -0.50734474
fBodyAccJerk-kurtosis()-Z -0.80553591
fBodyAccJerk-bandsEnergy()-1,8 -0.99998649
fBodyAccJerk-bandsEnergy()-9,16 -0.9999796
fBodyAccJerk-bandsEnergy()-17,24 -0.99997478
fBodyAccJerk-bandsEnergy()-25,32 -0.99995513
fBodyAccJerk-bandsEnergy()-33,40 -0.99991861
fBodyAccJerk-bandsEnergy()-41,48 -0.99964011
fBodyAccJerk-bandsEnergy()-49,56 -0.9994833
fBodyAccJerk-bandsEnergy()-57,64 -0.99996087
fBodyAccJerk-bandsEnergy()-1,16 -0.99998227
fBodyAccJerk-bandsEnergy()-17,32 -0.99997072
fBodyAccJerk-bandsEnergy()-33,48 -0.99981098
fBodyAccJerk-bandsEnergy()-49,64 -0.99948472
fBodyAccJerk-bandsEnergy()-1,24 -0.99998083
fBodyAccJerk-bandsEnergy()-25,48 -0.99985189
fBodyAccJerk-bandsEnergy()-1,8.1 -0.99993261
fBodyAccJerk-bandsEnergy()-9,16.1 -0.99989993
fBodyAccJerk-bandsEnergy()-17,24.1 -0.99982444
fBodyAccJerk-bandsEnergy()-25,32.1 -0.99985982
fBodyAccJerk-bandsEnergy()-33,40.1 -0.99972751
fBodyAccJerk-bandsEnergy()-41,48.1 -0.99972876
fBodyAccJerk-bandsEnergy()-49,56.1 -0.99956707
fBodyAccJerk-bandsEnergy()-57,64.1 -0.99976524
fBodyAccJerk-bandsEnergy()-1,16.1 -0.99990021
fBodyAccJerk-bandsEnergy()-17,32.1 -0.9998149
fBodyAccJerk-bandsEnergy()-33,48.1 -0.9997098
fBodyAccJerk-bandsEnergy()-49,64.1 -0.99959608
fBodyAccJerk-bandsEnergy()-1,24.1 -0.99985216
fBodyAccJerk-bandsEnergy()-25,48.1 -0.9998221
fBodyAccJerk-bandsEnergy()-1,8.2 -0.99939988
fBodyAccJerk-bandsEnergy()-9,16.2 -0.99976559
fBodyAccJerk-bandsEnergy()-17,24.2 -0.99995846
fBodyAccJerk-bandsEnergy()-25,32.2 -0.99994951
fBodyAccJerk-bandsEnergy()-33,40.2 -0.9998385
fBodyAccJerk-bandsEnergy()-41,48.2 -0.99981351
fBodyAccJerk-bandsEnergy()-49,56.2 -0.99878054
fBodyAccJerk-bandsEnergy()-57,64.2 -0.99857783
fBodyAccJerk-bandsEnergy()-1,16.2 -0.99961968
fBodyAccJerk-bandsEnergy()-17,32.2 -0.99998359
fBodyAccJerk-bandsEnergy()-33,48.2 -0.99982812
fBodyAccJerk-bandsEnergy()-49,64.2 -0.99868068
fBodyAccJerk-bandsEnergy()-1,24.2 -0.99984416
fBodyAccJerk-bandsEnergy()-25,48.2 -0.99992792
fBodyGyro-mean()-X -0.98657442
fBodyGyro-mean()-Y -0.98176153
fBodyGyro-mean()-Z -0.98951478
fBodyGyro-std()-X -0.98503264
fBodyGyro-std()-Y -0.97388607
fBodyGyro-std()-Z -0.99403493
fBodyGyro-mad()-X -0.98653085
fBodyGyro-mad()-Y -0.98361636
fBodyGyro-mad()-Z -0.99235201
fBodyGyro-max()-X -0.98049843
fBodyGyro-max()-Y -0.97227092
fBodyGyro-max()-Z -0.99494426
fBodyGyro-min()-X -0.99756862
fBodyGyro-min()-Y -0.9840851
fBodyGyro-min()-Z -0.99433541
fBodyGyro-sma() -0.98527621
fBodyGyro-energy()-X -0.99986371
fBodyGyro-energy()-Y -0.99966608
fBodyGyro-energy()-Z -0.99993462
fBodyGyro-iqr()-X -0.99034389
fBodyGyro-iqr()-Y -0.99483569
fBodyGyro-iqr()-Z -0.99441158
fBodyGyro-entropy()-X -0.71240225
fBodyGyro-entropy()-Y -0.64484236
fBodyGyro-entropy()-Z -0.83899298
fBodyGyro-maxInds-X -1.0
fBodyGyro-maxInds-Y -1.0
fBodyGyro-maxInds-Z -1.0
fBodyGyro-meanFreq()-X -0.25754888
fBodyGyro-meanFreq()-Y 0.097947109
fBodyGyro-meanFreq()-Z 0.54715105
fBodyGyro-skewness()-X 0.37731121
fBodyGyro-kurtosis()-X 0.13409154
fBodyGyro-skewness()-Y 0.27337197
fBodyGyro-kurtosis()-Y -0.091261831
fBodyGyro-skewness()-Z -0.4843465
fBodyGyro-kurtosis()-Z -0.7828507
fBodyGyro-bandsEnergy()-1,8 -0.99986502
fBodyGyro-bandsEnergy()-9,16 -0.99993178
fBodyGyro-bandsEnergy()-17,24 -0.99997295
fBodyGyro-bandsEnergy()-25,32 -0.99997018
fBodyGyro-bandsEnergy()-33,40 -0.99993012
fBodyGyro-bandsEnergy()-41,48 -0.99995862
fBodyGyro-bandsEnergy()-49,56 -0.99992899
fBodyGyro-bandsEnergy()-57,64 -0.99998465
fBodyGyro-bandsEnergy()-1,16 -0.99986326
fBodyGyro-bandsEnergy()-17,32 -0.99996815
fBodyGyro-bandsEnergy()-33,48 -0.9999361
fBodyGyro-bandsEnergy()-49,64 -0.99995363
fBodyGyro-bandsEnergy()-1,24 -0.99986442
fBodyGyro-bandsEnergy()-25,48 -0.99996098
fBodyGyro-bandsEnergy()-1,8.1 -0.99945373
fBodyGyro-bandsEnergy()-9,16.1 -0.99997811
fBodyGyro-bandsEnergy()-17,24.1 -0.99999153
fBodyGyro-bandsEnergy()-25,32.1 -0.9999901
fBodyGyro-bandsEnergy()-33,40.1 -0.99996857
fBodyGyro-bandsEnergy()-41,48.1 -0.99980657
fBodyGyro-bandsEnergy()-49,56.1 -0.998346
fBodyGyro-bandsEnergy()-57,64.1 -0.99896122
fBodyGyro-bandsEnergy()-1,16.1 -0.99961874
fBodyGyro-bandsEnergy()-17,32.1 -0.99998934
fBodyGyro-bandsEnergy()-33,48.1 -0.9999354
fBodyGyro-bandsEnergy()-49,64.1 -0.99838752
fBodyGyro-bandsEnergy()-1,24.1 -0.99964264
fBodyGyro-bandsEnergy()-25,48.1 -0.99997266
fBodyGyro-bandsEnergy()-1,8.2 -0.99995535
fBodyGyro-bandsEnergy()-9,16.2 -0.9999763
fBodyGyro-bandsEnergy()-17,24.2 -0.99990583
fBodyGyro-bandsEnergy()-25,32.2 -0.9999855
fBodyGyro-bandsEnergy()-33,40.2 -0.99993717
fBodyGyro-bandsEnergy()-41,48.2 -0.99975115
fBodyGyro-bandsEnergy()-49,56.2 -0.99907227
fBodyGyro-bandsEnergy()-57,64.2 -0.99992754
fBodyGyro-bandsEnergy()-1,16.2 -0.99995158
fBodyGyro-bandsEnergy()-17,32.2 -0.99990585
fBodyGyro-bandsEnergy()-33,48.2 -0.99989269
fBodyGyro-bandsEnergy()-49,64.2 -0.99944433
fBodyGyro-bandsEnergy()-1,24.2 -0.99994099
fBodyGyro-bandsEnergy()-25,48.2 -0.99995861
fBodyAccMag-mean() -0.95215466
fBodyAccMag-std() -0.95613397
fBodyAccMag-mad() -0.94887014
fBodyAccMag-max() -0.97432057
fBodyAccMag-min() -0.92572179
fBodyAccMag-sma() -0.95215466
fBodyAccMag-energy() -0.9982852
fBodyAccMag-iqr() -0.9732732
fBodyAccMag-entropy() -0.64637645
fBodyAccMag-maxInds -0.79310345
fBodyAccMag-meanFreq() -0.08843612
fBodyAccMag-skewness() -0.43647104
fBodyAccMag-kurtosis() -0.79684048
fBodyBodyAccJerkMag-mean() -0.99372565
fBodyBodyAccJerkMag-std() -0.99375495
fBodyBodyAccJerkMag-mad() -0.9919757
fBodyBodyAccJerkMag-max() -0.99336472
fBodyBodyAccJerkMag-min() -0.98817543
fBodyBodyAccJerkMag-sma() -0.99372565
fBodyBodyAccJerkMag-energy() -0.99991844
fBodyBodyAccJerkMag-iqr() -0.99136366
fBodyBodyAccJerkMag-entropy() -1.0
fBodyBodyAccJerkMag-maxInds -0.93650794
fBodyBodyAccJerkMag-meanFreq() 0.34698853
fBodyBodyAccJerkMag-skewness() -0.51608015
fBodyBodyAccJerkMag-kurtosis() -0.80276003
fBodyBodyGyroMag-mean() -0.98013485
fBodyBodyGyroMag-std() -0.96130944
fBodyBodyGyroMag-mad() -0.97365344
fBodyBodyGyroMag-max() -0.95226383
fBodyBodyGyroMag-min() -0.98949813
fBodyBodyGyroMag-sma() -0.98013485
fBodyBodyGyroMag-energy() -0.99924035
fBodyBodyGyroMag-iqr() -0.99265553
fBodyBodyGyroMag-entropy() -0.70129141
fBodyBodyGyroMag-maxInds -1.0
fBodyBodyGyroMag-meanFreq() -0.1289889
fBodyBodyGyroMag-skewness() 0.58615643
fBodyBodyGyroMag-kurtosis() 0.37460462
fBodyBodyGyroJerkMag-mean() -0.99199044
fBodyBodyGyroJerkMag-std() -0.99069746
fBodyBodyGyroJerkMag-mad() -0.98994084
fBodyBodyGyroJerkMag-max() -0.99244784
fBodyBodyGyroJerkMag-min() -0.99104773
fBodyBodyGyroJerkMag-sma() -0.99199044
fBodyBodyGyroJerkMag-energy() -0.99993676
fBodyBodyGyroJerkMag-iqr() -0.99045792
fBodyBodyGyroJerkMag-entropy() -0.8713058
fBodyBodyGyroJerkMag-maxInds -1.0
fBodyBodyGyroJerkMag-meanFreq() -0.074323027
fBodyBodyGyroJerkMag-skewness() -0.29867637
fBodyBodyGyroJerkMag-kurtosis() -0.71030407
angle(tBodyAccMean,gravity) -0.11275434
angle(tBodyAccJerkMean),gravityMean) 0.030400372
angle(tBodyGyroMean,gravityMean) -0.46476139
angle(tBodyGyroJerkMean,gravityMean) -0.018445884
angle(X,gravityMean) -0.84124676
angle(Y,gravityMean) 0.17994061
angle(Z,gravityMean) -0.058626924
subject 1.0

mega duzo kolumn
kolumna subject to numer ochotnika - raczej nielegalne informacje, trzeba wywalić

Jak zostały zebrane dane i co one oznaczają¶

Do zebrania danych zostały wykorzystane czujniki w telefonie (przyśpieszeniomierz, żyroskop), które zapisywały 3-osiowe przyśpieszenie liniowe(tAcc-XYZ) oraz 3-osiową prędkość kątową (tGyro-XYZ). Prefix 't' w metrykach oznacza czas.

Sygnał o przyśpieszeniu został rozdzielony na przyśpieszenie ciała i przyśpieszenie grawitacyjne (tBodyAcc-XYZ, tGravityAcc-XYZ).

Dodatkowo z przyśpieszenia i prędkości kątowej wyznaczono zryw (tBodyAccJerk-XYZ, tBodyGyroJerk-XYZ)

Obliczono wartość poszczególnych sygnałów przy pomocy normy Euklidesowej:

  • tBodyAccMag
  • tGravityAccMag
  • tBodyAccJerkMag
  • tBodyGyroMag
  • tBodyGyroJerkMag

Prefix 'f' oznacza, że jest domeną frekwencyjną po zastosowaniu Szybkiej Transformacji Fouriera, czyli przekształcenia ciągu próbej sygnału $(a_0, a_1,..., a_{N-1})$, $a_i \in \R$ w ciąg harmoniczny $(A_0, A_1,...,A_{N-1})$, $A_i \in \R$, zgodne ze wzorem: $$ A_k = \sum^{N-1}_{n=0} a_n w_N ^{-kn}, 0\le k \le N-1, w_N = e^{i\frac{2\pi}{N}} $$ , gdzie i - liczba urojona, k - nr harmonicznej, n - nr próbki sygnału, $a_n$ - wartość próbki sygnału, N - liczba próbek.

Wszystkie dostępne sygnały:

  • tBodyAcc
  • tGravityAccMag
  • tBodyGyroJerkMag
  • tBodyAccJerk
  • tBodyGyro
  • tBodyAccJerkMag
  • tBodyGyroJerk
  • tBodyAccMag
  • tGravityAcc
  • tBodyGyroMag
  • fBodyAcc
  • fBodyBodyGyroMag
  • fBodyAccJerk
  • fBodyBodyAccJerkMag
  • fBodyAccMag
  • fBodyGyro
  • fBodyBodyGyroJerkMag
In [53]:
data = data.drop('subject', axis=1)
our_data = our_data.drop('subject', axis=1)
validator_data = validator_data.drop('subject', axis=1)
In [54]:
our_data.to_csv('../data/our_data.csv', index=False)
validator_data.to_csv('../data/validator_data.csv', index=False)
In [55]:
data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10299 entries, 0 to 10298
Columns: 561 entries, tBodyAcc-mean()-X to angle(Z,gravityMean)
dtypes: float64(561)
memory usage: 44.1 MB
In [56]:
data.shape
Out[56]:
(10299, 561)
In [57]:
data.describe()
Out[57]:
tBodyAcc-mean()-X tBodyAcc-mean()-Y tBodyAcc-mean()-Z tBodyAcc-std()-X tBodyAcc-std()-Y tBodyAcc-std()-Z tBodyAcc-mad()-X tBodyAcc-mad()-Y tBodyAcc-mad()-Z tBodyAcc-max()-X ... fBodyBodyGyroJerkMag-meanFreq() fBodyBodyGyroJerkMag-skewness() fBodyBodyGyroJerkMag-kurtosis() angle(tBodyAccMean,gravity) angle(tBodyAccJerkMean),gravityMean) angle(tBodyGyroMean,gravityMean) angle(tBodyGyroJerkMean,gravityMean) angle(X,gravityMean) angle(Y,gravityMean) angle(Z,gravityMean)
count 10299.000000 10299.000000 10299.000000 10299.000000 10299.000000 10299.000000 10299.000000 10299.000000 10299.000000 10299.000000 ... 10299.000000 10299.000000 10299.000000 10299.000000 10299.000000 10299.000000 10299.000000 10299.000000 10299.000000 10299.000000
mean 0.274347 -0.017743 -0.108925 -0.607784 -0.510191 -0.613064 -0.633593 -0.525697 -0.614989 -0.466732 ... 0.126708 -0.298592 -0.617700 0.007705 0.002648 0.017683 -0.009219 -0.496522 0.063255 -0.054284
std 0.067628 0.037128 0.053033 0.438694 0.500240 0.403657 0.413333 0.484201 0.399034 0.538707 ... 0.245443 0.320199 0.308796 0.336591 0.447364 0.616188 0.484770 0.511158 0.305468 0.268898
min -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 ... -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000
25% 0.262625 -0.024902 -0.121019 -0.992360 -0.976990 -0.979137 -0.993293 -0.977017 -0.979064 -0.935788 ... -0.019481 -0.536174 -0.841847 -0.124694 -0.287031 -0.493108 -0.389041 -0.817288 0.002151 -0.131880
50% 0.277174 -0.017162 -0.108596 -0.943030 -0.835032 -0.850773 -0.948244 -0.843670 -0.845068 -0.874825 ... 0.136245 -0.335160 -0.703402 0.008146 0.007668 0.017192 -0.007186 -0.715631 0.182028 -0.003882
75% 0.288354 -0.010625 -0.097589 -0.250293 -0.057336 -0.278737 -0.302033 -0.087405 -0.288149 -0.014641 ... 0.288960 -0.113167 -0.487981 0.149005 0.291490 0.536137 0.365996 -0.521503 0.250790 0.102970
max 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 ... 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000

8 rows × 561 columns

In [58]:
data.isna().sum().sum()
Out[58]:
0
In [59]:
data.duplicated().sum()
Out[59]:
0
In [60]:
data = our_data

nie ma duplikatow, nie ma wartości NA

In [61]:
#columns with variances
for column in data.columns:
    print(column + ' variance: ' + str(data[column].var()))
tBodyAcc-mean()-X variance: 0.0044769421018401164
tBodyAcc-mean()-Y variance: 0.0013073837539268314
tBodyAcc-mean()-Z variance: 0.0027694192760906685
tBodyAcc-std()-X variance: 0.1925145874633604
tBodyAcc-std()-Y variance: 0.2500234056668236
tBodyAcc-std()-Z variance: 0.16317495619877742
tBodyAcc-mad()-X variance: 0.17104109761769018
tBodyAcc-mad()-Y variance: 0.23392930786117078
tBodyAcc-mad()-Z variance: 0.15925895593970477
tBodyAcc-max()-X variance: 0.28980073061210443
tBodyAcc-max()-Y variance: 0.0783974216221772
tBodyAcc-max()-Z variance: 0.08009845958439489
tBodyAcc-min()-X variance: 0.12626623345624427
tBodyAcc-min()-Y variance: 0.11516030415949183
tBodyAcc-min()-Z variance: 0.0851457185762241
tBodyAcc-sma() variance: 0.21282353332234252
tBodyAcc-energy()-X variance: 0.06130929887210224
tBodyAcc-energy()-Y variance: 0.015620590506499237
tBodyAcc-energy()-Z variance: 0.042629911965941326
tBodyAcc-iqr()-X variance: 0.12929907140304242
tBodyAcc-iqr()-Y variance: 0.13550404180670667
tBodyAcc-iqr()-Z variance: 0.13802162331687137
tBodyAcc-entropy()-X variance: 0.2140492037240372
tBodyAcc-entropy()-Y variance: 0.18732332852241745
tBodyAcc-entropy()-Z variance: 0.1342684321248004
tBodyAcc-arCoeff()-X,1 variance: 0.0948414383496001
tBodyAcc-arCoeff()-X,2 variance: 0.06079429393582888
tBodyAcc-arCoeff()-X,3 variance: 0.061074745199654765
tBodyAcc-arCoeff()-X,4 variance: 0.053390542150113955
tBodyAcc-arCoeff()-Y,1 variance: 0.06388026695475865
tBodyAcc-arCoeff()-Y,2 variance: 0.04507507775145629
tBodyAcc-arCoeff()-Y,3 variance: 0.0433245157407126
tBodyAcc-arCoeff()-Y,4 variance: 0.04872749089065405
tBodyAcc-arCoeff()-Z,1 variance: 0.07890871360908631
tBodyAcc-arCoeff()-Z,2 variance: 0.046315234156252244
tBodyAcc-arCoeff()-Z,3 variance: 0.05574323892116306
tBodyAcc-arCoeff()-Z,4 variance: 0.05217893517859387
tBodyAcc-correlation()-X,Y variance: 0.12868741850115376
tBodyAcc-correlation()-X,Z variance: 0.10613257911568273
tBodyAcc-correlation()-Y,Z variance: 0.1428173950084769
tGravityAcc-mean()-X variance: 0.26119283211115724
tGravityAcc-mean()-Y variance: 0.14354652444117244
tGravityAcc-mean()-Z variance: 0.11058150218461149
tGravityAcc-std()-X variance: 0.005585243951767919
tGravityAcc-std()-Y variance: 0.007087955712434068
tGravityAcc-std()-Z variance: 0.009984117451470528
tGravityAcc-mad()-X variance: 0.005374241928028323
tGravityAcc-mad()-Y variance: 0.006889667280456189
tGravityAcc-mad()-Z variance: 0.009769062416794544
tGravityAcc-max()-X variance: 0.2547809711493905
tGravityAcc-max()-Y variance: 0.13435920566499165
tGravityAcc-max()-Z variance: 0.10749808670944473
tGravityAcc-min()-X variance: 0.252576622207885
tGravityAcc-min()-Y variance: 0.14165065753858258
tGravityAcc-min()-Z variance: 0.11222901950221918
tGravityAcc-sma() variance: 0.15221271912207224
tGravityAcc-energy()-X variance: 0.48062549291042816
tGravityAcc-energy()-Y variance: 0.20633463735234914
tGravityAcc-energy()-Z variance: 0.17606999508840906
tGravityAcc-iqr()-X variance: 0.004823924624644773
tGravityAcc-iqr()-Y variance: 0.0062561661734055005
tGravityAcc-iqr()-Z variance: 0.008874297130694766
tGravityAcc-entropy()-X variance: 0.13060044204300483
tGravityAcc-entropy()-Y variance: 0.07881709359491207
tGravityAcc-entropy()-Z variance: 0.15494499076632529
tGravityAcc-arCoeff()-X,1 variance: 0.046616644723727695
tGravityAcc-arCoeff()-X,2 variance: 0.043864112824116196
tGravityAcc-arCoeff()-X,3 variance: 0.043114806063649994
tGravityAcc-arCoeff()-X,4 variance: 0.044294547152750136
tGravityAcc-arCoeff()-Y,1 variance: 0.08510984181073537
tGravityAcc-arCoeff()-Y,2 variance: 0.08839127806474463
tGravityAcc-arCoeff()-Y,3 variance: 0.08375534481092596
tGravityAcc-arCoeff()-Y,4 variance: 0.07883361156891049
tGravityAcc-arCoeff()-Z,1 variance: 0.07164088372801314
tGravityAcc-arCoeff()-Z,2 variance: 0.0669291046694802
tGravityAcc-arCoeff()-Z,3 variance: 0.06378614451312242
tGravityAcc-arCoeff()-Z,4 variance: 0.06198864602083171
tGravityAcc-correlation()-X,Y variance: 0.48611656625829164
tGravityAcc-correlation()-X,Z variance: 0.4962658502056412
tGravityAcc-correlation()-Y,Z variance: 0.49241811393933693
tBodyAccJerk-mean()-X variance: 0.03099463167602009
tBodyAccJerk-mean()-Y variance: 0.027195642907117294
tBodyAccJerk-mean()-Z variance: 0.024249179038239525
tBodyAccJerk-std()-X variance: 0.16586271638033623
tBodyAccJerk-std()-Y variance: 0.18728524287794135
tBodyAccJerk-std()-Z variance: 0.07682020849228101
tBodyAccJerk-mad()-X variance: 0.17012896033680533
tBodyAccJerk-mad()-Y variance: 0.19913841884393885
tBodyAccJerk-mad()-Z variance: 0.08004200516158354
tBodyAccJerk-max()-X variance: 0.12793846731804348
tBodyAccJerk-max()-Y variance: 0.08676480507159709
tBodyAccJerk-max()-Z variance: 0.0499912875678026
tBodyAccJerk-min()-X variance: 0.18877009088054233
tBodyAccJerk-min()-Y variance: 0.12803282275044528
tBodyAccJerk-min()-Z variance: 0.09391796938758086
tBodyAccJerk-sma() variance: 0.15372057621725846
tBodyAccJerk-energy()-X variance: 0.0457138945027856
tBodyAccJerk-energy()-Y variance: 0.05957802170614774
tBodyAccJerk-energy()-Z variance: 0.013629326774191835
tBodyAccJerk-iqr()-X variance: 0.18390578349998593
tBodyAccJerk-iqr()-Y variance: 0.1409118403820026
tBodyAccJerk-iqr()-Z variance: 0.07024445818496423
tBodyAccJerk-entropy()-X variance: 0.4250574027599599
tBodyAccJerk-entropy()-Y variance: 0.3986348977807441
tBodyAccJerk-entropy()-Z variance: 0.3693758412362016
tBodyAccJerk-arCoeff()-X,1 variance: 0.09201627955021724
tBodyAccJerk-arCoeff()-X,2 variance: 0.03688828804159097
tBodyAccJerk-arCoeff()-X,3 variance: 0.05841863245116189
tBodyAccJerk-arCoeff()-X,4 variance: 0.03857935466853554
tBodyAccJerk-arCoeff()-Y,1 variance: 0.07097740700165883
tBodyAccJerk-arCoeff()-Y,2 variance: 0.04704483168131824
tBodyAccJerk-arCoeff()-Y,3 variance: 0.056390837814098184
tBodyAccJerk-arCoeff()-Y,4 variance: 0.04123905739477185
tBodyAccJerk-arCoeff()-Z,1 variance: 0.07327766822967441
tBodyAccJerk-arCoeff()-Z,2 variance: 0.038650895989761944
tBodyAccJerk-arCoeff()-Z,3 variance: 0.04932771198321671
tBodyAccJerk-arCoeff()-Z,4 variance: 0.057422860147315256
tBodyAccJerk-correlation()-X,Y variance: 0.06732082198080272
tBodyAccJerk-correlation()-X,Z variance: 0.08583053219679816
tBodyAccJerk-correlation()-Y,Z variance: 0.0757253265572673
tBodyGyro-mean()-X variance: 0.03401948423535753
tBodyGyro-mean()-Y variance: 0.01792968572581448
tBodyGyro-mean()-Z variance: 0.01795841962651189
tBodyGyro-std()-X variance: 0.09107630944742055
tBodyGyro-std()-Y variance: 0.125861263259997
tBodyGyro-std()-Z variance: 0.13950567110311113
tBodyGyro-mad()-X variance: 0.08903099584342154
tBodyGyro-mad()-Y variance: 0.11722642426721408
tBodyGyro-mad()-Z variance: 0.13168508711009588
tBodyGyro-max()-X variance: 0.07706050712775221
tBodyGyro-max()-Y variance: 0.069807631350714
tBodyGyro-max()-Z variance: 0.09267567176064244
tBodyGyro-min()-X variance: 0.06376422996141676
tBodyGyro-min()-Y variance: 0.04405604125578248
tBodyGyro-min()-Z variance: 0.09491938398868191
tBodyGyro-sma() variance: 0.16168717836590835
tBodyGyro-energy()-X variance: 0.020808135990968247
tBodyGyro-energy()-Y variance: 0.03526885118499151
tBodyGyro-energy()-Z variance: 0.03650740730891167
tBodyGyro-iqr()-X variance: 0.0918052894489914
tBodyGyro-iqr()-Y variance: 0.1043406325563918
tBodyGyro-iqr()-Z variance: 0.10108679847957205
tBodyGyro-entropy()-X variance: 0.20731747806158213
tBodyGyro-entropy()-Y variance: 0.13696450966881693
tBodyGyro-entropy()-Z variance: 0.20672442174307706
tBodyGyro-arCoeff()-X,1 variance: 0.07592136562353444
tBodyGyro-arCoeff()-X,2 variance: 0.05135225460885689
tBodyGyro-arCoeff()-X,3 variance: 0.05259559757937087
tBodyGyro-arCoeff()-X,4 variance: 0.05860014260699311
tBodyGyro-arCoeff()-Y,1 variance: 0.04308135822783237
tBodyGyro-arCoeff()-Y,2 variance: 0.03641994810051895
tBodyGyro-arCoeff()-Y,3 variance: 0.05168538864069485
tBodyGyro-arCoeff()-Y,4 variance: 0.04677002823097223
tBodyGyro-arCoeff()-Z,1 variance: 0.09789678928597803
tBodyGyro-arCoeff()-Z,2 variance: 0.07988933444151035
tBodyGyro-arCoeff()-Z,3 variance: 0.0720508560467873
tBodyGyro-arCoeff()-Z,4 variance: 0.06684542471175238
tBodyGyro-correlation()-X,Y variance: 0.14479692971951272
tBodyGyro-correlation()-X,Z variance: 0.1481607260735439
tBodyGyro-correlation()-Y,Z variance: 0.17328354951440553
tBodyGyroJerk-mean()-X variance: 0.016621110926878954
tBodyGyroJerk-mean()-Y variance: 0.012932609901319824
tBodyGyroJerk-mean()-Z variance: 0.01639661687251594
tBodyGyroJerk-std()-X variance: 0.0930770473744336
tBodyGyroJerk-std()-Y variance: 0.07328262480478664
tBodyGyroJerk-std()-Z variance: 0.09138512190072078
tBodyGyroJerk-mad()-X variance: 0.09468324819797247
tBodyGyroJerk-mad()-Y variance: 0.06539268706068921
tBodyGyroJerk-mad()-Z variance: 0.0857992152838401
tBodyGyroJerk-max()-X variance: 0.08585521799590187
tBodyGyroJerk-max()-Y variance: 0.06344211474922835
tBodyGyroJerk-max()-Z variance: 0.09421256287884862
tBodyGyroJerk-min()-X variance: 0.07901016181827171
tBodyGyroJerk-min()-Y variance: 0.050676412316502537
tBodyGyroJerk-min()-Z variance: 0.062150133786041506
tBodyGyroJerk-sma() variance: 0.07211516548214168
tBodyGyroJerk-energy()-X variance: 0.018295011568934764
tBodyGyroJerk-energy()-Y variance: 0.017040429017863276
tBodyGyroJerk-energy()-Z variance: 0.01815356215178411
tBodyGyroJerk-iqr()-X variance: 0.09168856438444567
tBodyGyroJerk-iqr()-Y variance: 0.057684769513897616
tBodyGyroJerk-iqr()-Z variance: 0.0736965517625947
tBodyGyroJerk-entropy()-X variance: 0.3176628578059097
tBodyGyroJerk-entropy()-Y variance: 0.29635770107721965
tBodyGyroJerk-entropy()-Z variance: 0.3368254087879385
tBodyGyroJerk-arCoeff()-X,1 variance: 0.061868345053234346
tBodyGyroJerk-arCoeff()-X,2 variance: 0.034653556125095096
tBodyGyroJerk-arCoeff()-X,3 variance: 0.044275946396582076
tBodyGyroJerk-arCoeff()-X,4 variance: 0.04479865526389249
tBodyGyroJerk-arCoeff()-Y,1 variance: 0.0452957285943773
tBodyGyroJerk-arCoeff()-Y,2 variance: 0.027872675612099287
tBodyGyroJerk-arCoeff()-Y,3 variance: 0.038732745112459076
tBodyGyroJerk-arCoeff()-Y,4 variance: 0.06032404169887483
tBodyGyroJerk-arCoeff()-Z,1 variance: 0.11796076463468294
tBodyGyroJerk-arCoeff()-Z,2 variance: 0.05395404829324188
tBodyGyroJerk-arCoeff()-Z,3 variance: 0.06108062584572923
tBodyGyroJerk-arCoeff()-Z,4 variance: 0.06155361666695325
tBodyGyroJerk-correlation()-X,Y variance: 0.07631687791784077
tBodyGyroJerk-correlation()-X,Z variance: 0.07027767624536363
tBodyGyroJerk-correlation()-Y,Z variance: 0.06737083576597896
tBodyAccMag-mean() variance: 0.21821054021982741
tBodyAccMag-std() variance: 0.18409706053673333
tBodyAccMag-mad() variance: 0.1399736194502291
tBodyAccMag-max() variance: 0.2132807468001808
tBodyAccMag-min() variance: 0.03620316454253036
tBodyAccMag-sma() variance: 0.21821054021982741
tBodyAccMag-energy() variance: 0.07878119529689388
tBodyAccMag-iqr() variance: 0.09832731854155205
tBodyAccMag-entropy() variance: 0.4468045665430065
tBodyAccMag-arCoeff()1 variance: 0.08401775130928239
tBodyAccMag-arCoeff()2 variance: 0.05483315918939998
tBodyAccMag-arCoeff()3 variance: 0.0637065419808146
tBodyAccMag-arCoeff()4 variance: 0.0688129863209937
tGravityAccMag-mean() variance: 0.21821054021982741
tGravityAccMag-std() variance: 0.18409706053673333
tGravityAccMag-mad() variance: 0.1399736194502291
tGravityAccMag-max() variance: 0.2132807468001808
tGravityAccMag-min() variance: 0.03620316454253036
tGravityAccMag-sma() variance: 0.21821054021982741
tGravityAccMag-energy() variance: 0.07878119529689388
tGravityAccMag-iqr() variance: 0.09832731854155205
tGravityAccMag-entropy() variance: 0.4468045665430065
tGravityAccMag-arCoeff()1 variance: 0.08401775130928239
tGravityAccMag-arCoeff()2 variance: 0.05483315918939998
tGravityAccMag-arCoeff()3 variance: 0.0637065419808146
tGravityAccMag-arCoeff()4 variance: 0.0688129863209937
tBodyAccJerkMag-mean() variance: 0.1510826893974949
tBodyAccJerkMag-std() variance: 0.17286870285130101
tBodyAccJerkMag-mad() variance: 0.15712103611348635
tBodyAccJerkMag-max() variance: 0.1619681171686266
tBodyAccJerkMag-min() variance: 0.0650885145222443
tBodyAccJerkMag-sma() variance: 0.1510826893974949
tBodyAccJerkMag-energy() variance: 0.04215218986476785
tBodyAccJerkMag-iqr() variance: 0.11710930812181335
tBodyAccJerkMag-entropy() variance: 0.523554748660506
tBodyAccJerkMag-arCoeff()1 variance: 0.05911154612879264
tBodyAccJerkMag-arCoeff()2 variance: 0.05982163496941966
tBodyAccJerkMag-arCoeff()3 variance: 0.05786532442764301
tBodyAccJerkMag-arCoeff()4 variance: 0.06735846772629919
tBodyGyroMag-mean() variance: 0.1596720200912777
tBodyGyroMag-std() variance: 0.12182080683711213
tBodyGyroMag-mad() variance: 0.1448849093236813
tBodyGyroMag-max() variance: 0.10161664772248963
tBodyGyroMag-min() variance: 0.09769292705225238
tBodyGyroMag-sma() variance: 0.1596720200912777
tBodyGyroMag-energy() variance: 0.04888900730292003
tBodyGyroMag-iqr() variance: 0.1295348600033986
tBodyGyroMag-entropy() variance: 0.23246526369405152
tBodyGyroMag-arCoeff()1 variance: 0.08757477369718733
tBodyGyroMag-arCoeff()2 variance: 0.07090210745304783
tBodyGyroMag-arCoeff()3 variance: 0.06333372149805352
tBodyGyroMag-arCoeff()4 variance: 0.06272022110858624
tBodyGyroJerkMag-mean() variance: 0.07593704083188658
tBodyGyroJerkMag-std() variance: 0.07323890086652206
tBodyGyroJerkMag-mad() variance: 0.062495061328663504
tBodyGyroJerkMag-max() variance: 0.07115244574100077
tBodyGyroJerkMag-min() variance: 0.06156129858268948
tBodyGyroJerkMag-sma() variance: 0.07593704083188658
tBodyGyroJerkMag-energy() variance: 0.014498435295459765
tBodyGyroJerkMag-iqr() variance: 0.05339057651073691
tBodyGyroJerkMag-entropy() variance: 0.4786859948086381
tBodyGyroJerkMag-arCoeff()1 variance: 0.0570737254544036
tBodyGyroJerkMag-arCoeff()2 variance: 0.043057561592672204
tBodyGyroJerkMag-arCoeff()3 variance: 0.051603635343599685
tBodyGyroJerkMag-arCoeff()4 variance: 0.06264963013681654
fBodyAcc-mean()-X variance: 0.17620049108501215
fBodyAcc-mean()-Y variance: 0.23155664211097182
fBodyAcc-mean()-Z variance: 0.12836606043181653
fBodyAcc-std()-X variance: 0.20004254496403195
fBodyAcc-std()-Y variance: 0.23027176888969544
fBodyAcc-std()-Z variance: 0.159757365148621
fBodyAcc-mad()-X variance: 0.20867076373107318
fBodyAcc-mad()-Y variance: 0.2461145771701687
fBodyAcc-mad()-Z variance: 0.1516610920170035
fBodyAcc-max()-X variance: 0.16564466518248028
fBodyAcc-max()-Y variance: 0.12507511408126354
fBodyAcc-max()-Z variance: 0.1528670770366282
fBodyAcc-min()-X variance: 0.05103581883158596
fBodyAcc-min()-Y variance: 0.032412894324738026
fBodyAcc-min()-Z variance: 0.01678977083504041
fBodyAcc-sma() variance: 0.22177023922421213
fBodyAcc-energy()-X variance: 0.06126143004211884
fBodyAcc-energy()-Y variance: 0.09840695485178359
fBodyAcc-energy()-Z variance: 0.05130099867037763
fBodyAcc-iqr()-X variance: 0.1580760886029029
fBodyAcc-iqr()-Y variance: 0.15231597573274552
fBodyAcc-iqr()-Z variance: 0.08401167760880253
fBodyAcc-entropy()-X variance: 0.5210502075320648
fBodyAcc-entropy()-Y variance: 0.44179422771404436
fBodyAcc-entropy()-Z variance: 0.3718011918685037
fBodyAcc-maxInds-X variance: 0.0698670126235585
fBodyAcc-maxInds-Y variance: 0.05950292122980049
fBodyAcc-maxInds-Z variance: 0.058359215219027344
fBodyAcc-meanFreq()-X variance: 0.07024375838333395
fBodyAcc-meanFreq()-Y variance: 0.05757650895276057
fBodyAcc-meanFreq()-Z variance: 0.08072816365251509
fBodyAcc-skewness()-X variance: 0.1613047195828785
fBodyAcc-kurtosis()-X variance: 0.1952393941181204
fBodyAcc-skewness()-Y variance: 0.12720204087308673
fBodyAcc-kurtosis()-Y variance: 0.14891024412338724
fBodyAcc-skewness()-Z variance: 0.1604929403017108
fBodyAcc-kurtosis()-Z variance: 0.16898194833628355
fBodyAcc-bandsEnergy()-1,8 variance: 0.07238759870011621
fBodyAcc-bandsEnergy()-9,16 variance: 0.03080378133830322
fBodyAcc-bandsEnergy()-17,24 variance: 0.049938829899136596
fBodyAcc-bandsEnergy()-25,32 variance: 0.0327164438372514
fBodyAcc-bandsEnergy()-33,40 variance: 0.019809664351880207
fBodyAcc-bandsEnergy()-41,48 variance: 0.02230304474886189
fBodyAcc-bandsEnergy()-49,56 variance: 0.010628570655686087
fBodyAcc-bandsEnergy()-57,64 variance: 0.014105546086979231
fBodyAcc-bandsEnergy()-1,16 variance: 0.06635876633757386
fBodyAcc-bandsEnergy()-17,32 variance: 0.05593857031857847
fBodyAcc-bandsEnergy()-33,48 variance: 0.019348748260329368
fBodyAcc-bandsEnergy()-49,64 variance: 0.01098265273977148
fBodyAcc-bandsEnergy()-1,24 variance: 0.06273730671383304
fBodyAcc-bandsEnergy()-25,48 variance: 0.03547599612806119
fBodyAcc-bandsEnergy()-1,8.1 variance: 0.07481340630992561
fBodyAcc-bandsEnergy()-9,16.1 variance: 0.05739621269958564
fBodyAcc-bandsEnergy()-17,24.1 variance: 0.048402879865634026
fBodyAcc-bandsEnergy()-25,32.1 variance: 0.02715588234719452
fBodyAcc-bandsEnergy()-33,40.1 variance: 0.03033634693683529
fBodyAcc-bandsEnergy()-41,48.1 variance: 0.0350775634366622
fBodyAcc-bandsEnergy()-49,56.1 variance: 0.028304092888693906
fBodyAcc-bandsEnergy()-57,64.1 variance: 0.016780546638190855
fBodyAcc-bandsEnergy()-1,16.1 variance: 0.0950071730465999
fBodyAcc-bandsEnergy()-17,32.1 variance: 0.06319810702624862
fBodyAcc-bandsEnergy()-33,48.1 variance: 0.03651580950555846
fBodyAcc-bandsEnergy()-49,64.1 variance: 0.022524363599338973
fBodyAcc-bandsEnergy()-1,24.1 variance: 0.09675182878395555
fBodyAcc-bandsEnergy()-25,48.1 variance: 0.030368017558191154
fBodyAcc-bandsEnergy()-1,8.2 variance: 0.04675605921555085
fBodyAcc-bandsEnergy()-9,16.2 variance: 0.03070034859912075
fBodyAcc-bandsEnergy()-17,24.2 variance: 0.020542299591896913
fBodyAcc-bandsEnergy()-25,32.2 variance: 0.006182878541287872
fBodyAcc-bandsEnergy()-33,40.2 variance: 0.00560915266223008
fBodyAcc-bandsEnergy()-41,48.2 variance: 0.013541715825299254
fBodyAcc-bandsEnergy()-49,56.2 variance: 0.011581790203825508
fBodyAcc-bandsEnergy()-57,64.2 variance: 0.012650742698531309
fBodyAcc-bandsEnergy()-1,16.2 variance: 0.045218015512478865
fBodyAcc-bandsEnergy()-17,32.2 variance: 0.013189430430581068
fBodyAcc-bandsEnergy()-33,48.2 variance: 0.007683831698815254
fBodyAcc-bandsEnergy()-49,64.2 variance: 0.010758590645850101
fBodyAcc-bandsEnergy()-1,24.2 variance: 0.049643888598150644
fBodyAcc-bandsEnergy()-25,48.2 variance: 0.00619283227209873
fBodyAccJerk-mean()-X variance: 0.15070652197924725
fBodyAccJerk-mean()-Y variance: 0.16575641178018785
fBodyAccJerk-mean()-Z variance: 0.08808846556388308
fBodyAccJerk-std()-X variance: 0.1539147603940635
fBodyAccJerk-std()-Y variance: 0.18788415998897146
fBodyAccJerk-std()-Z variance: 0.06707721373877917
fBodyAccJerk-mad()-X variance: 0.20971935812533224
fBodyAccJerk-mad()-Y variance: 0.17465976890554846
fBodyAccJerk-mad()-Z variance: 0.07599133900603627
fBodyAccJerk-max()-X variance: 0.11444653339280422
fBodyAccJerk-max()-Y variance: 0.1330133911965325
fBodyAccJerk-max()-Z variance: 0.05939345066234401
fBodyAccJerk-min()-X variance: 0.03472798016560612
fBodyAccJerk-min()-Y variance: 0.046227192269697626
fBodyAccJerk-min()-Z variance: 0.032143264059482805
fBodyAccJerk-sma() variance: 0.17634307429820362
fBodyAccJerk-energy()-X variance: 0.045833443454325884
fBodyAccJerk-energy()-Y variance: 0.05953324883268387
fBodyAccJerk-energy()-Z variance: 0.013623591865885964
fBodyAccJerk-iqr()-X variance: 0.17542697482174513
fBodyAccJerk-iqr()-Y variance: 0.09768761956082063
fBodyAccJerk-iqr()-Z variance: 0.07062446493967557
fBodyAccJerk-entropy()-X variance: 0.5600704138790288
fBodyAccJerk-entropy()-Y variance: 0.5379347564460603
fBodyAccJerk-entropy()-Z variance: 0.4074890648988625
fBodyAccJerk-maxInds-X variance: 0.10435294515414725
fBodyAccJerk-maxInds-Y variance: 0.0701849696967341
fBodyAccJerk-maxInds-Z variance: 0.08590974988802902
fBodyAccJerk-meanFreq()-X variance: 0.08739592533398391
fBodyAccJerk-meanFreq()-Y variance: 0.07317232424028697
fBodyAccJerk-meanFreq()-Z variance: 0.07409022611740908
fBodyAccJerk-skewness()-X variance: 0.0668323160129934
fBodyAccJerk-kurtosis()-X variance: 0.04468564147062609
fBodyAccJerk-skewness()-Y variance: 0.03557914507541991
fBodyAccJerk-kurtosis()-Y variance: 0.02017944807478049
fBodyAccJerk-skewness()-Z variance: 0.04020468871774056
fBodyAccJerk-kurtosis()-Z variance: 0.022946699718231094
fBodyAccJerk-bandsEnergy()-1,8 variance: 0.04453097847763921
fBodyAccJerk-bandsEnergy()-9,16 variance: 0.029847697165176386
fBodyAccJerk-bandsEnergy()-17,24 variance: 0.040477624611083514
fBodyAccJerk-bandsEnergy()-25,32 variance: 0.031775988800925185
fBodyAccJerk-bandsEnergy()-33,40 variance: 0.017138559412520935
fBodyAccJerk-bandsEnergy()-41,48 variance: 0.026387746743091425
fBodyAccJerk-bandsEnergy()-49,56 variance: 0.010221083942420798
fBodyAccJerk-bandsEnergy()-57,64 variance: 0.002714807081025747
fBodyAccJerk-bandsEnergy()-1,16 variance: 0.03736419960926148
fBodyAccJerk-bandsEnergy()-17,32 variance: 0.05156040977746052
fBodyAccJerk-bandsEnergy()-33,48 variance: 0.02209469475939296
fBodyAccJerk-bandsEnergy()-49,64 variance: 0.010945234046959375
fBodyAccJerk-bandsEnergy()-1,24 variance: 0.0478666130924042
fBodyAccJerk-bandsEnergy()-25,48 variance: 0.04922959832269986
fBodyAccJerk-bandsEnergy()-1,8.1 variance: 0.055596688420752265
fBodyAccJerk-bandsEnergy()-9,16.1 variance: 0.043169098220662935
fBodyAccJerk-bandsEnergy()-17,24.1 variance: 0.06631824503418884
fBodyAccJerk-bandsEnergy()-25,32.1 variance: 0.024668069216722516
fBodyAccJerk-bandsEnergy()-33,40.1 variance: 0.02130546077584993
fBodyAccJerk-bandsEnergy()-41,48.1 variance: 0.03992551319742578
fBodyAccJerk-bandsEnergy()-49,56.1 variance: 0.01677312955398876
fBodyAccJerk-bandsEnergy()-57,64.1 variance: 0.008648954970739113
fBodyAccJerk-bandsEnergy()-1,16.1 variance: 0.05437786140775746
fBodyAccJerk-bandsEnergy()-17,32.1 variance: 0.06304735090615839
fBodyAccJerk-bandsEnergy()-33,48.1 variance: 0.03794310199287232
fBodyAccJerk-bandsEnergy()-49,64.1 variance: 0.014242531673827313
fBodyAccJerk-bandsEnergy()-1,24.1 variance: 0.07097448187585614
fBodyAccJerk-bandsEnergy()-25,48.1 variance: 0.02765759482644583
fBodyAccJerk-bandsEnergy()-1,8.2 variance: 0.027101885895900537
fBodyAccJerk-bandsEnergy()-9,16.2 variance: 0.03154979231033198
fBodyAccJerk-bandsEnergy()-17,24.2 variance: 0.01729144970949675
fBodyAccJerk-bandsEnergy()-25,32.2 variance: 0.005945491394425677
fBodyAccJerk-bandsEnergy()-33,40.2 variance: 0.004995985986168579
fBodyAccJerk-bandsEnergy()-41,48.2 variance: 0.012377866437112504
fBodyAccJerk-bandsEnergy()-49,56.2 variance: 0.01665979715089601
fBodyAccJerk-bandsEnergy()-57,64.2 variance: 0.009016311990507937
fBodyAccJerk-bandsEnergy()-1,16.2 variance: 0.039427941862873975
fBodyAccJerk-bandsEnergy()-17,32.2 variance: 0.010009085664863982
fBodyAccJerk-bandsEnergy()-33,48.2 variance: 0.0071265437493440095
fBodyAccJerk-bandsEnergy()-49,64.2 variance: 0.016477365995092034
fBodyAccJerk-bandsEnergy()-1,24.2 variance: 0.02799157512760521
fBodyAccJerk-bandsEnergy()-25,48.2 variance: 0.006005011449334394
fBodyGyro-mean()-X variance: 0.12425841988085169
fBodyGyro-mean()-Y variance: 0.1110442062267809
fBodyGyro-mean()-Z variance: 0.1458722357091164
fBodyGyro-std()-X variance: 0.08206923622471465
fBodyGyro-std()-Y variance: 0.1352346541271663
fBodyGyro-std()-Z variance: 0.11457627364762704
fBodyGyro-mad()-X variance: 0.11230251123240358
fBodyGyro-mad()-Y variance: 0.10489264471168488
fBodyGyro-mad()-Z variance: 0.1490763532763312
fBodyGyro-max()-X variance: 0.09285927639359372
fBodyGyro-max()-Y variance: 0.10533026682020001
fBodyGyro-max()-Z variance: 0.07604083660126311
fBodyGyro-min()-X variance: 0.012754629686748826
fBodyGyro-min()-Y variance: 0.02438893932017824
fBodyGyro-min()-Z variance: 0.019763815780703804
fBodyGyro-sma() variance: 0.1303211429464363
fBodyGyro-energy()-X variance: 0.017152848182698962
fBodyGyro-energy()-Y variance: 0.035628924884671696
fBodyGyro-energy()-Z variance: 0.03987962996582353
fBodyGyro-iqr()-X variance: 0.11378132487225272
fBodyGyro-iqr()-Y variance: 0.09097429241179016
fBodyGyro-iqr()-Z variance: 0.1236435730609421
fBodyGyro-entropy()-X variance: 0.369748691485735
fBodyGyro-entropy()-Y variance: 0.37137297651690737
fBodyGyro-entropy()-Z variance: 0.3625669175668779
fBodyGyro-maxInds-X variance: 0.03596993479702613
fBodyGyro-maxInds-Y variance: 0.08267228756467052
fBodyGyro-maxInds-Z variance: 0.054324438085144186
fBodyGyro-meanFreq()-X variance: 0.06482591398246966
fBodyGyro-meanFreq()-Y variance: 0.07460712542629397
fBodyGyro-meanFreq()-Z variance: 0.0704103881337236
fBodyGyro-skewness()-X variance: 0.10437364560641044
fBodyGyro-kurtosis()-X variance: 0.11588116724449628
fBodyGyro-skewness()-Y variance: 0.12045898335047683
fBodyGyro-kurtosis()-Y variance: 0.14251109840825638
fBodyGyro-skewness()-Z variance: 0.11029025929318306
fBodyGyro-kurtosis()-Z variance: 0.12105992554498364
fBodyGyro-bandsEnergy()-1,8 variance: 0.013963062026664218
fBodyGyro-bandsEnergy()-9,16 variance: 0.025364034345867544
fBodyGyro-bandsEnergy()-17,24 variance: 0.019586007957239297
fBodyGyro-bandsEnergy()-25,32 variance: 0.006882683692325504
fBodyGyro-bandsEnergy()-33,40 variance: 0.01184724710025043
fBodyGyro-bandsEnergy()-41,48 variance: 0.008642375051335825
fBodyGyro-bandsEnergy()-49,56 variance: 0.006714892424487017
fBodyGyro-bandsEnergy()-57,64 variance: 0.005317954900239434
fBodyGyro-bandsEnergy()-1,16 variance: 0.015982640309287095
fBodyGyro-bandsEnergy()-17,32 variance: 0.01962906407320969
fBodyGyro-bandsEnergy()-33,48 variance: 0.011978962709321772
fBodyGyro-bandsEnergy()-49,64 variance: 0.005826304477666091
fBodyGyro-bandsEnergy()-1,24 variance: 0.016711515867859034
fBodyGyro-bandsEnergy()-25,48 variance: 0.007863112754786376
fBodyGyro-bandsEnergy()-1,8.1 variance: 0.046545591422753996
fBodyGyro-bandsEnergy()-9,16.1 variance: 0.010149007851245817
fBodyGyro-bandsEnergy()-17,24.1 variance: 0.01261057045658394
fBodyGyro-bandsEnergy()-25,32.1 variance: 0.006896208147389761
fBodyGyro-bandsEnergy()-33,40.1 variance: 0.0034802287752631
fBodyGyro-bandsEnergy()-41,48.1 variance: 0.008868098891297877
fBodyGyro-bandsEnergy()-49,56.1 variance: 0.012267887474151123
fBodyGyro-bandsEnergy()-57,64.1 variance: 0.00580507368294781
fBodyGyro-bandsEnergy()-1,16.1 variance: 0.03360665018625851
fBodyGyro-bandsEnergy()-17,32.1 variance: 0.01527430221000299
fBodyGyro-bandsEnergy()-33,48.1 variance: 0.004216412026657845
fBodyGyro-bandsEnergy()-49,64.1 variance: 0.01094511102445687
fBodyGyro-bandsEnergy()-1,24.1 variance: 0.03999738713786341
fBodyGyro-bandsEnergy()-25,48.1 variance: 0.00666430455750655
fBodyGyro-bandsEnergy()-1,8.2 variance: 0.029193088250484136
fBodyGyro-bandsEnergy()-9,16.2 variance: 0.017927759057571362
fBodyGyro-bandsEnergy()-17,24.2 variance: 0.01888437609020458
fBodyGyro-bandsEnergy()-25,32.2 variance: 0.005648181206527622
fBodyGyro-bandsEnergy()-33,40.2 variance: 0.004348621931241796
fBodyGyro-bandsEnergy()-41,48.2 variance: 0.006176934335451651
fBodyGyro-bandsEnergy()-49,56.2 variance: 0.011047976352854429
fBodyGyro-bandsEnergy()-57,64.2 variance: 0.008329489549744438
fBodyGyro-bandsEnergy()-1,16.2 variance: 0.03542011369842754
fBodyGyro-bandsEnergy()-17,32.2 variance: 0.025295047038539444
fBodyGyro-bandsEnergy()-33,48.2 variance: 0.004563521776722529
fBodyGyro-bandsEnergy()-49,64.2 variance: 0.009202730974715238
fBodyGyro-bandsEnergy()-1,24.2 variance: 0.03848340888221707
fBodyGyro-bandsEnergy()-25,48.2 variance: 0.005004281723665741
fBodyAccMag-mean() variance: 0.19744241991363984
fBodyAccMag-std() variance: 0.12635038451167352
fBodyAccMag-mad() variance: 0.18415740883923254
fBodyAccMag-max() variance: 0.0667298625060265
fBodyAccMag-min() variance: 0.024877929116656414
fBodyAccMag-sma() variance: 0.19744241991363984
fBodyAccMag-energy() variance: 0.06082934541673548
fBodyAccMag-iqr() variance: 0.11894280797441864
fBodyAccMag-entropy() variance: 0.45897389838240277
fBodyAccMag-maxInds variance: 0.06850248458427
fBodyAccMag-meanFreq() variance: 0.06882951452210465
fBodyAccMag-skewness() variance: 0.10271468146537477
fBodyAccMag-kurtosis() variance: 0.10093035713954598
fBodyBodyAccJerkMag-mean() variance: 0.1794370276310095
fBodyBodyAccJerkMag-std() variance: 0.1637197641921945
fBodyBodyAccJerkMag-mad() variance: 0.1844863731768773
fBodyBodyAccJerkMag-max() variance: 0.13627663760994213
fBodyBodyAccJerkMag-min() variance: 0.07210586132914354
fBodyBodyAccJerkMag-sma() variance: 0.1794370276310095
fBodyBodyAccJerkMag-energy() variance: 0.051498013073888814
fBodyBodyAccJerkMag-iqr() variance: 0.13310524980230382
fBodyBodyAccJerkMag-entropy() variance: 0.44312360155605013
fBodyBodyAccJerkMag-maxInds variance: 0.034980593548474444
fBodyBodyAccJerkMag-meanFreq() variance: 0.06288259066473362
fBodyBodyAccJerkMag-skewness() variance: 0.13379221142709152
fBodyBodyAccJerkMag-kurtosis() variance: 0.1261789218892486
fBodyBodyGyroMag-mean() variance: 0.10432726722704622
fBodyBodyGyroMag-std() variance: 0.09648458368892042
fBodyBodyGyroMag-mad() variance: 0.10976948261690145
fBodyBodyGyroMag-max() variance: 0.07907077036280058
fBodyBodyGyroMag-min() variance: 0.026751208252962448
fBodyBodyGyroMag-sma() variance: 0.10432726722704622
fBodyBodyGyroMag-energy() variance: 0.03260213889669768
fBodyBodyGyroMag-iqr() variance: 0.09614727223887459
fBodyBodyGyroMag-entropy() variance: 0.3624935745221581
fBodyBodyGyroMag-maxInds variance: 0.02558548810345983
fBodyBodyGyroMag-meanFreq() variance: 0.07804496389090335
fBodyBodyGyroMag-skewness() variance: 0.10339994669655914
fBodyBodyGyroMag-kurtosis() variance: 0.10195292018655154
fBodyBodyGyroJerkMag-mean() variance: 0.07080176375343458
fBodyBodyGyroJerkMag-std() variance: 0.06709234324886033
fBodyBodyGyroJerkMag-mad() variance: 0.07800145906977704
fBodyBodyGyroJerkMag-max() variance: 0.0589548954432414
fBodyBodyGyroJerkMag-min() variance: 0.03634454108884741
fBodyBodyGyroJerkMag-sma() variance: 0.07080176375343458
fBodyBodyGyroJerkMag-energy() variance: 0.01626321832853924
fBodyBodyGyroJerkMag-iqr() variance: 0.0770973458674192
fBodyBodyGyroJerkMag-entropy() variance: 0.38826912122814766
fBodyBodyGyroJerkMag-maxInds variance: 0.01990033006089468
fBodyBodyGyroJerkMag-meanFreq() variance: 0.059722875548427326
fBodyBodyGyroJerkMag-skewness() variance: 0.10423887776141551
fBodyBodyGyroJerkMag-kurtosis() variance: 0.09736195823596573
angle(tBodyAccMean,gravity) variance: 0.11353362835650334
angle(tBodyAccJerkMean),gravityMean) variance: 0.19998342356266013
angle(tBodyGyroMean,gravityMean) variance: 0.38381257790020445
angle(tBodyGyroJerkMean,gravityMean) variance: 0.238903350439246
angle(X,gravityMean) variance: 0.2570861163389007
angle(Y,gravityMean) variance: 0.09344515621016526
angle(Z,gravityMean) variance: 0.07138322841986906

moze usunąć, te kolumny, ktore mają 'małą' wariancję?

In [62]:
for column in data.columns:
    if data[column].var() < 0.005:
        print(column + ' variance: ' + str(data[column].var()))
tBodyAcc-mean()-X variance: 0.0044769421018401164
tBodyAcc-mean()-Y variance: 0.0013073837539268314
tBodyAcc-mean()-Z variance: 0.0027694192760906685
tGravityAcc-iqr()-X variance: 0.004823924624644773
fBodyAccJerk-bandsEnergy()-57,64 variance: 0.002714807081025747
fBodyAccJerk-bandsEnergy()-33,40.2 variance: 0.004995985986168579
fBodyGyro-bandsEnergy()-33,40.1 variance: 0.0034802287752631
fBodyGyro-bandsEnergy()-33,48.1 variance: 0.004216412026657845
fBodyGyro-bandsEnergy()-33,40.2 variance: 0.004348621931241796
fBodyGyro-bandsEnergy()-33,48.2 variance: 0.004563521776722529

zadna z kolumn nie ma wariancji = 0, trudno stwierdzic czy warto usuwac

In [63]:
atributes = set()
static_methods = set()
for col in data.columns:
    atributes.add(col.split("-")[0])
    try:  
        static_methods.add(col.split("-")[1])
    except IndexError:
        continue

for atr in atributes:
    print(atr + "\n")
tBodyAccMag

tBodyAccJerk

angle(tBodyAccJerkMean),gravityMean)

tBodyAcc

angle(Y,gravityMean)

fBodyAcc

tBodyGyroMag

tBodyAccJerkMag

fBodyGyro

angle(tBodyGyroMean,gravityMean)

fBodyAccMag

tGravityAcc

fBodyBodyAccJerkMag

fBodyBodyGyroMag

angle(Z,gravityMean)

fBodyAccJerk

angle(tBodyAccMean,gravity)

tGravityAccMag

tBodyGyro

tBodyGyroJerkMag

angle(X,gravityMean)

angle(tBodyGyroJerkMean,gravityMean)

tBodyGyroJerk

fBodyBodyGyroJerkMag

In [64]:
for stat in static_methods:
    print(stat + "\n")
skewness()

meanFreq()

maxInds

max()

correlation()

arCoeff()

arCoeff()3

bandsEnergy()

arCoeff()4

min()

mad()

sma()

iqr()

std()

entropy()

energy()

arCoeff()2

arCoeff()1

mean()

kurtosis()

Funkcje do estymacji sygnałów¶

  • max()
  • mad() - mediana odchylenia bezwzględnego
  • min()
  • kurtosis() - jedna z miar kształtu rozkładu częstotliwości sygnału
  • bandsEnergy() - energia przedziału częstotliwości w przedziałach FFT każdego okna
  • mean()
  • meanFreq()
  • arCoeff() - współczynnik autoregresji
  • entropy() - entropia sygnału, średnia ilość informacji przypadająca na pojedynczą wiadomość ze źródła
  • iqr() - $Q_3 - Q_1 $
  • sma() - obszar wielkości sygnału
  • std()
  • maxInds - indeks składowej częstotliwości o największej wartości
  • skewness()
  • energy()
  • correlation()

Patrzymy na rozkłady dla interesujących nas statystyk

In [65]:
stat = 'mean'
stat_cols = [col for col in data.columns if stat in col]
In [66]:
data[stat_cols].hist(figsize = (50,50))
Out[66]:
array([[<Axes: title={'center': 'tBodyAcc-mean()-X'}>,
        <Axes: title={'center': 'tBodyAcc-mean()-Y'}>,
        <Axes: title={'center': 'tBodyAcc-mean()-Z'}>,
        <Axes: title={'center': 'tGravityAcc-mean()-X'}>,
        <Axes: title={'center': 'tGravityAcc-mean()-Y'}>,
        <Axes: title={'center': 'tGravityAcc-mean()-Z'}>,
        <Axes: title={'center': 'tBodyAccJerk-mean()-X'}>],
       [<Axes: title={'center': 'tBodyAccJerk-mean()-Y'}>,
        <Axes: title={'center': 'tBodyAccJerk-mean()-Z'}>,
        <Axes: title={'center': 'tBodyGyro-mean()-X'}>,
        <Axes: title={'center': 'tBodyGyro-mean()-Y'}>,
        <Axes: title={'center': 'tBodyGyro-mean()-Z'}>,
        <Axes: title={'center': 'tBodyGyroJerk-mean()-X'}>,
        <Axes: title={'center': 'tBodyGyroJerk-mean()-Y'}>],
       [<Axes: title={'center': 'tBodyGyroJerk-mean()-Z'}>,
        <Axes: title={'center': 'tBodyAccMag-mean()'}>,
        <Axes: title={'center': 'tGravityAccMag-mean()'}>,
        <Axes: title={'center': 'tBodyAccJerkMag-mean()'}>,
        <Axes: title={'center': 'tBodyGyroMag-mean()'}>,
        <Axes: title={'center': 'tBodyGyroJerkMag-mean()'}>,
        <Axes: title={'center': 'fBodyAcc-mean()-X'}>],
       [<Axes: title={'center': 'fBodyAcc-mean()-Y'}>,
        <Axes: title={'center': 'fBodyAcc-mean()-Z'}>,
        <Axes: title={'center': 'fBodyAcc-meanFreq()-X'}>,
        <Axes: title={'center': 'fBodyAcc-meanFreq()-Y'}>,
        <Axes: title={'center': 'fBodyAcc-meanFreq()-Z'}>,
        <Axes: title={'center': 'fBodyAccJerk-mean()-X'}>,
        <Axes: title={'center': 'fBodyAccJerk-mean()-Y'}>],
       [<Axes: title={'center': 'fBodyAccJerk-mean()-Z'}>,
        <Axes: title={'center': 'fBodyAccJerk-meanFreq()-X'}>,
        <Axes: title={'center': 'fBodyAccJerk-meanFreq()-Y'}>,
        <Axes: title={'center': 'fBodyAccJerk-meanFreq()-Z'}>,
        <Axes: title={'center': 'fBodyGyro-mean()-X'}>,
        <Axes: title={'center': 'fBodyGyro-mean()-Y'}>,
        <Axes: title={'center': 'fBodyGyro-mean()-Z'}>],
       [<Axes: title={'center': 'fBodyGyro-meanFreq()-X'}>,
        <Axes: title={'center': 'fBodyGyro-meanFreq()-Y'}>,
        <Axes: title={'center': 'fBodyGyro-meanFreq()-Z'}>,
        <Axes: title={'center': 'fBodyAccMag-mean()'}>,
        <Axes: title={'center': 'fBodyAccMag-meanFreq()'}>,
        <Axes: title={'center': 'fBodyBodyAccJerkMag-mean()'}>,
        <Axes: title={'center': 'fBodyBodyAccJerkMag-meanFreq()'}>],
       [<Axes: title={'center': 'fBodyBodyGyroMag-mean()'}>,
        <Axes: title={'center': 'fBodyBodyGyroMag-meanFreq()'}>,
        <Axes: title={'center': 'fBodyBodyGyroJerkMag-mean()'}>,
        <Axes: title={'center': 'fBodyBodyGyroJerkMag-meanFreq()'}>,
        <Axes: >, <Axes: >, <Axes: >]], dtype=object)
No description has been provided for this image

widać duzo róznych rozkładów.

3. macierz korelacji¶

3.1 mean¶

In [67]:
plt.figure(figsize=(40,40))
sns.heatmap(data[stat_cols].corr(),annot=True, cmap='coolwarm')
plt.title('Correlation matrix for "mean"')
plt.show()
No description has been provided for this image

bardzo duże korelacje niektórych kolumn, czy je usuwać? (z doświadczenia usuwanie to nie jest najlepsza opcja)

3.2 entropy¶

In [68]:
stat = 'entropy'
stat_cols1 = [col for col in data.columns if stat in col]
In [69]:
plt.figure(figsize=(40,40))
sns.heatmap(data[stat_cols1].corr(),annot=True, cmap='coolwarm')
plt.title('Correlation matrix for "entropy"')

plt.show()
No description has been provided for this image

bardzo duze korelacje entropii. Moze nie jest ona potrzebna/znacząca?

4. Pairplot¶

4.1 mean¶

In [70]:
sns.pairplot(data[stat_cols[:10]])
Out[70]:
<seaborn.axisgrid.PairGrid at 0x30bf7a490>
No description has been provided for this image

widać czasami jakiś podział

In [71]:
#save plot
plt.savefig('../EDA/pairplot.png')
<Figure size 640x480 with 0 Axes>

4.2 entropy¶

In [72]:
sns.pairplot(data[stat_cols1[:10]])
Out[72]:
<seaborn.axisgrid.PairGrid at 0x321f83e90>
No description has been provided for this image

widać podział mimo wysokich korelacji

5. boxplot¶

5.1 mean¶

In [73]:
fig, axes = plt.subplots(10, 5, figsize=(40, 50))
axes = axes.flatten()

for i, col in enumerate(stat_cols):
    sns.boxplot(data, x= data[col], ax= axes[i])

plt.tight_layout() 
plt.show()
No description has been provided for this image

5.2 entropy¶

In [74]:
fig, axes = plt.subplots(7, 5, figsize=(40, 50))
axes = axes.flatten()

for i, col in enumerate(stat_cols1):
    sns.boxplot(data, x= data[col], ax= axes[i])

plt.tight_layout() 
plt.show()
No description has been provided for this image

6. TSNE¶

In [75]:
#tsne
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import seaborn as sns

tsne = TSNE(n_components=2, random_state=42)
tsne_results = tsne.fit_transform(data)
#plot results 
plt.figure(figsize=(10,5))
sns.scatterplot(x=tsne_results[:,0], y=tsne_results[:,1])
Out[75]:
<Axes: >
No description has been provided for this image

widać cos na kształt klastra
tam po prawej troche 'ni pies ni wydra, cos na ksztalt świdra'